Improving Design of Systems Supporting Creativity-intensive Processes – A Cross-industry Focus Group Evaluation

Organizations depend on the creative potential of their members to continuously develop innovative solutions. Groups commonly approach creative processes using collaborative IT. However, current design of information systems does not cater to the business processes representing the context in which groups operate. Creativity-intensive processes are a conceptualization of business processes that involve creativity. Voigt, Bergener, and Becker (2013) developed an explanatory design theory for information systems supporting creativity-intensive processes. The core component of the design theory is an information system architecture for creativity-intensive process support systems (CPSS). This paper evaluates the utility of the CPSS architecture to comprehensively support creativity-intensive processes. Three exploratory cross-industry focus groups, in which the architecture instantiation CreativeFlow was demonstrated, suggest that the features of CreativeFlow and the underlying architectural concepts are useful in supporting practitioners’ processes, especially for the support of creative group processes. However, three modifications to the CPSS architecture emerge: increased freedom for choosing individuals responsible for group tasks, differentiated authorization for creating and assigning creative group tasks, and advanced communication support for initiation of standard workflows. The evaluation further contributes recommendations for tool features and four research issues to advance system design of tools supporting creativity in business processes. The study provides insights for future information system evaluations in Design Science Research on Information Systems

Model Reduction of Parametric Differential-Algebraic Systems by Balanced Truncation

In this article, we deduce a procedure to apply balanced truncation to parameter-dependent differential-algebraic systems. For that we solve multiple projected Lyapunov equations for different parameter values to compute the Gramians that are required for the truncation procedure. As this process would lead to high computational costs if we perform it for a large number of parameters. Hence, we combine this approach with the reduced basis method that determines a reduced representation of the Lyapunov equation solutions for the parameters of interest. Residual-based error estimators are then used to evaluate the quality of the approximations. After introducing the procedure for a general class of differential-algebraic systems we turn our focus to systems with a certain structure, for which the method can be applied particularly efficiently. We illustrate the effectiveness of our approach on several models from fluid dynamics and mechanics. We further consider an application of the method in the context of damping optimization

Structure Preserving Model Order Reduction by Parameter Optimization

Model order reduction (MOR) methods that are designed to preserve structural features of a given full order model (FOM) often suffer from a lower accuracy when compared to their non structure preserving counterparts. In this paper, we present a framework for MOR based on direct parameter optimization. This means that the elements of the system matrices are iteratively varied to minimize an objective functional that measures the difference between the FOM and the reduced order model (ROM). Structural constraints are encoded in the parametrization of the ROM. The method only depends on frequency response data and can thus be applied to a wide range of dynamical systems. We illustrate the effectiveness of our method on a port-Hamiltonian and on a symmetric second order system in a comparison with other structure preserving MOR algorithms.Comment: 26 pages, 7 figure